def __init__(self):
self.rotation_speed = 1 # higher is faster, positive is right
self.cam_z = -2
self.values = []
for x in range(7):
for r in range(23):
if x == 6 and r > 21:
break
Label(text=r + 1 + (x * 23), relief=RIDGE,
width=5).grid(row=r, column=0 + (x * 2))
s = Scale(master,
from_=0.,
to=1.,
resolution=0.1,
orient=HORIZONTAL)
s.set(1)
s.grid(row=r, column=1 + (x * 2)) # length=10,
self.values.append(s)
Button(master, text='max', command=self.max).grid(row=16,
column=14,
columnspan=2)
Button(master, text='randomize',
command=self.randomize).grid(row=17, column=14, columnspan=2)
Button(master, text='rotate right',
command=self.right).grid(row=18, column=14, columnspan=2)
Button(master, text='rotate left',
command=self.left).grid(row=19, column=14, columnspan=2)
Button(master, text='cam up', command=self.cam_up).grid(row=20,
column=14,
columnspan=2)
Button(master, text='cam down',
command=self.cam_down).grid(row=21, column=14, columnspan=2)
Button(master, text='print config',
command=self.print_config).grid(row=22, column=14, columnspan=2)
self.b = Button(master, text="enter values", command=self.popup)
self.b.grid(row=17, column=14, columnspan=2)
width = 512
height = 512
self.renderer = Renderer(width, height)
self.image = Image.fromarray(np.zeros((width, height), dtype=np.uint8))
self.canvas = Canvas(master, height=height, width=width)
self.canvas.grid(row=0, column=14, rowspan=15)
# image = image.resize((basewidth, hsize), PIL.Image.ANTIALIAS)
self.photo = ImageTk.PhotoImage(self.image)
self.photo_holder = self.canvas.create_image(
width - (self.image.size[0] / 2),
height - (self.image.size[1] / 2),
image=self.photo)
self.rot = 0
self.render()
self.conv1 = nn.Conv2d(3, 32, (3, 3), (1, 1), (1, 1))
self.conv2 = nn.Conv2d(32, 64, (3, 3), (2, 2), (1, 1))
self.linear3 = nn.Linear(64 * 32 * 32, num_outputs)
self.linear3_ = nn.Linear(64 * 32 * 32, num_outputs)
def forward(self, inputs):
x = self.relu(self.conv1(inputs))
x = self.relu(self.conv2(x))
x = x.view(-1, 64 * 32 * 32)
mu = torch.sigmoid(self.linear3(x))
sigma_sq = torch.tanh(self.linear3_(x))
return mu, sigma_sq
env = Renderer(WIDTH, HEIGHT)
data_generator = ConstantShapeGenerator(WIDTH, HEIGHT)
torch.manual_seed(SEED)
np.random.seed(SEED)
agent = REINFORCE(HIDDEN_SIZE, WIDTH * HEIGHT * 3, 160, Policy)
if not os.path.exists(exp_dir):
os.mkdir(exp_dir)
reward_avg = []
for i_episode in range(NUM_EPISODES):
target = data_generator.sample()
x = self.relu(self.conv3(x))
# mu, logprob
return torch.sigmoid(self.linear1_mu(x.view(
-1, 128 * 16 * 16))), torch.sigmoid(
self.linear1_stddev(x.view(-1, 128 * 16 * 16)))
def decode(self, z):
x = self.relu(self.linear2(z))
return torch.sigmoid(self.linear3(x))
def forward(self, x):
raise NotImplementedError("shouldn't use this directly")
env = Renderer(params["WIDTH"], params["HEIGHT"])
# data_generator = CubeSingleViewGenerator(params["WIDTH"], params["HEIGHT"])
data_generator = RotatingRandomShapeGenerator(params["WIDTH"],
params["HEIGHT"])
torch.manual_seed(params["SEED"])
np.random.seed(params["SEED"])
policy = Policy(params["LATENT_SIZE"], 160).to(device)
optimizer = torch.optim.Adam(policy.parameters(), lr=params["LR"])
eps = np.finfo(np.float32).eps.item()
if not os.path.exists(exp_dir):
os.mkdir(exp_dir)
def __init__(self, width, height):
self.cube = Renderer(width, height, "cube", False)
self.sphere = Renderer(width, height, "sphere", True)
self.shape = np.ones(160) * 0.9
def __init__(self, width, height, radius=.5):
self.renderer = Renderer(width, height, "sphere", True)
self.shape = np.ones(160) * radius
def __init__(self, width, height):
self.renderer = Renderer(width, height, "sphere", True)
self.cam = np.random.uniform(-1, 1, 3)
def __init__(self, width, height, smin=.4, smax=.8):
self.renderer = Renderer(width, height, "sphere", True)
self.shape = np.random.uniform(smin, smax, 160)
self.cam = None
def __init__(self, width, height, smin=0, smax=1):
self.renderer = Renderer(width, height, "sphere", True)
self.cam = np.random.uniform(-1, 1, 3)
self.min = smin
self.max = smax
def __init__(self, width, height):
self.renderer = Renderer(width, height, "cube", False)
self.cam = None
def __init__(self, width, height):
self.renderer = Renderer(width, height, "cube", False)
self.cam = np.random.uniform(-1, 1, 3)
def __init__(self, width, height, smin=.5, smax=1):
self.renderer = Renderer(width, height, "iqtt", True)
self.shape = np.random.uniform(smin, smax, 160)
wandb.init(project="rezende-30", config=params)
# experiment = Experiment(api_key="ZfKpzyaedH6ajYSiKmvaSwyCs",
# project_name="rezende", workspace="fgolemo")
# experiment.log_parameters(params)
# experiment.set_name(exp_name)
# experiment.add_tag("v3")
def normal(x, mu, sigma_sq):
a = (-1 * (x - mu).pow(2) / (2 * sigma_sq)).exp()
b = 1 / (2 * sigma_sq * pi.expand_as(sigma_sq)).sqrt()
return a * b
env = Renderer(params["WIDTH"], params["HEIGHT"], shape="ijcv")
data_generator = CubeLoader()
torch.manual_seed(params["SEED"])
np.random.seed(params["SEED"])
policy = ReinforcePolicy(params["LATENT_SIZE"], 160).to(device)
if LOGGING:
wandb.watch(policy)
optimizer = torch.optim.Adam(policy.parameters(), lr=params["LR"])
eps = np.finfo(np.float32).eps.item()
if not os.path.exists(exp_dir):
os.mkdir(exp_dir)
from threedee_tools.datasets import CubeLoader from threedee_tools.renderer import Renderer import numpy as np import matplotlib.pyplot as plt env = Renderer(128, 128, shape="ijcv") gen = CubeLoader() imga = gen.sample() print(gen.cam) print(gen.light) env.base_light = -gen.light + 1 imgb = env.render(np.ones((160)), np.array([0, 0, 0]), cam_pos=gen.cam + .7) imgb = np.array(imgb, dtype=np.float32) / 255 imgab = np.zeros((128, 128 * 2, 3), dtype=np.float32) imgab[:, :128, :] = imga imgab[:, 128:, :] = imgb plt.imshow(imgab) plt.show()
